NequIP is a code for building E(3)-equivariant interatomic potentials

Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic potentials

🏆 A ranked list of awesome atomistic machine learning projects ⚛️🧬💎.

[ICLR'23 Spotlight] Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs

[ICLR'24] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

A Python library and command line interface for automated free energy calculations

UF3: a python library for generating ultra-fast interatomic potentials

python library for atomistic machine learning

KIM-based Learning-Integrated Fitting Framework for interatomic potentials.

FLAME: a library for atomistic modeling environments

An open source Julia library for active learning of interatomic potentials in atomistic simulations of materials. It incorporates elements of bayesian inference, machine learning, differentiable programming, software composability, and high-performance computing.

A flexible and performant framework for training machine learning potentials.

Fitting interatomic potential for molecular dynamics

Provide easy-to-use CESMIX-aligned case studies. Integrate the latest developments of the Julia atomistic ecosystem and state-of-the-art tools.

Modified Embedded Atom Method with Bond Order (MEAM-BO) implementation in LAMMPS

Generator of polynomial machine learning potentials

Automatic Differentiation of Interatomic Potentials with Phonons in Julia: ADIP².jl

A python package for fast building amorphous solids and liquid mixtures from https://materialsproject.org computed structures and machine learning interatomic potentials

Webpage for RANN interatomic potential

Instructions and scripts for adaptive sampling for gas-surface dynamics